Journal of investigative medicine : the official publication of the American Federation for Clinical Research
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Comparative Study
Treatment and outcomes among patients ≥85 years hospitalized with community-acquired pneumonia.
Our objective was to describe community-acquired pneumonia (CAP) among patients ≥85 years and compare them to patients aged 65-74. This was a retrospective cohort study. The study setting included 638 hospitals in the USA participating in the Premier database from 2010 to 2015. ⋯ In adjusted models, patients aged ≥85 had greater in-hospital mortality (OR 1.14, 95% CI 1.11 to 1.18), but were less likely to be admitted to the intensive care unit (OR 0.54, 95% CI 0.53 to 0.55) and receive mechanical ventilation (OR 0.47, 95% CI 0.46 to 0.48). They also had lower rates of acute kidney injury (OR 0.95, 95% CI 0.91 to 1.00) and Clostridium difficile infection (OR 0.91, 95% CI 0.85 to 0.99), shorter lengths of stay (mean multiplier 0.93, 95% CI 0.92 to 0.93) and lower cost (mean multiplier 0.81, 95% CI 0.80 to 0.81), and were more likely to be discharged to a skilled nursing facility (OR 2.19, 95% CI 2.15 to 2.24) or hospice (OR 2.19, 95% CI 2.11 to 2.27). In conclusion, patients aged ≥85 have different comorbidities and etiologies of CAP, receive less intense treatment, and have greater mortality than patients between 65 and 75 years.
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Hospitalized patients with COVID-19 must have a safe discharge plan to prevent readmissions. We assessed patients with COVID-19 admitted to hospitals belonging to a single health system between April 2020 and June 2020. Demographics, vitals and laboratory data were obtained by electronic data query and discharge processes were reviewed by manual abstraction. ⋯ Age, duration of intensive care unit stay, disposition destinations other than home, incomplete discharge planning and no arrangement for home oxygen may be associated with 14-day readmissions in patients with COVID-19. Certain clinical parameters on discharge day, while statistically different, may not reach clinically discriminant thresholds. Structured discharge processes may improve outcomes.
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Amyloidosis is a rare group of diseases characterized by abnormal folding of proteins and extracellular deposition of insoluble fibrils. It can be localized to one organ system or can have systemic involvement. The kidney is the most common organ to be involved in systemic amyloidosis often leading to renal failure and the nephrotic syndrome. ⋯ A renal biopsy along with characteristic features found on immunohistochemistry and mass spectrometry is diagnostic of ALECT2. ALECT2 should be suspected when all markers for AL and AA are negative. Proper diagnosis of ALECT2 can determine need for supportive care versus more aggressive interventions.
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Early studies have reported various electrolyte abnormalities at admission in patients with severe COVID-19. 104 out of 193 patients admitted to our institution presented with hypermagnesemia at presentation. It is believed this may be important in the evaluation of severe SARS-CoV-2 infections. This study evaluated the outcomes of hypermagnesemia in patients with COVID-19. ⋯ With age-adjusted logistic regression analysis hypermagnesemia was associated with mortality (p=0.007). This study demonstrates that hypermagnesemia is a significant marker of disease severity and adverse outcome in SARS-CoV-2 infections. We recommend serum magnesium be added to the panel of tests routinely ordered in evaluation of severe SARS-CoV-2 infections.
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AI relates broadly to the science of developing computer systems to imitate human intelligence, thus allowing for the automation of tasks that would otherwise necessitate human cognition. Such technology has increasingly demonstrated capacity to outperform humans for functions relating to image recognition. Given the current lack of cost-effective confirmatory testing, accurate diagnosis and subsequent management depend on visual detection of characteristic findings during otoscope examination. The aim of this manuscript is to perform a comprehensive literature review and evaluate the potential application of artificial intelligence for the diagnosis of ear disease from otoscopic image analysis.